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Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

Author

Listed:
  • Ricardo Francisco Reier Forradellas

    (Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain)

  • Sergio Luis Náñez Alonso

    (Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain)

  • Javier Jorge-Vazquez

    (Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain)

  • Marcela Laura Rodriguez

    (Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain)

Abstract

This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. The crimes reported (period 2016–2019) that occurred in the city of Buenos Aires selected to test the model are: homicides, theft, injuries, and robberies. With this, it is possible to generate a crime prediction model according to the city area based on the SEMMA (Sample, Explore, Modify, Model, and Assess) model and after data manipulation, standardization and cleaning; clustering is performed using K-means and subsequently the neural network is generated. For prediction, it is necessary to provide the model with the information corresponding to the predictive characteristics (predict); these characteristics being according to the developed neural network model: year, month, day, time zone, commune, and type of crime.

Suggested Citation

  • Ricardo Francisco Reier Forradellas & Sergio Luis Náñez Alonso & Javier Jorge-Vazquez & Marcela Laura Rodriguez, 2020. "Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction," Social Sciences, MDPI, vol. 10(1), pages 1-20, December.
    • Handle: RePEc:gam:jscscx:v:10:y:2020:i:1:p:4-:d:469820
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    References listed on IDEAS

    as
    1. Sergio Luis Náñez Alonso & Miguel Ángel Echarte Fernández & David Sanz Bas & Jarosław Kaczmarek, 2020. "Reasons Fostering or Discouraging the Implementation of Central Bank-Backed Digital Currency: A Review," Economies, MDPI, vol. 8(2), pages 1-27, May.
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    Cited by:

    1. Sergio Luis Nañez Alonso & Ricardo Francisco Reier Forradellas & Oriol Pi Morell & Javier Jorge-Vazquez, 2021. "Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    2. Miguel G. Folgado & Veronica Sanz, 2022. "Exploring the political pulse of a country using data science tools," Journal of Computational Social Science, Springer, vol. 5(1), pages 987-1000, May.

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